A method of diagnosing diagnostic target data includes defining normal data as learning data in advance and diagnosing whether the diagnostic target data are normal on the basis of whether the learning data include a waveform similar to the waveform of the diagnostic target data. For example, sensor data acquired during normal operation of production equipment are used as learning data, and sensor data of the production equipment in operation are used as diagnostic target data, whereby an abnormality in the production equipment can be detected.
Whether the learning data include a waveform similar to the waveform of the diagnostic target data can be determined by using the dissimilarity between subsequences extracted from the learning data and the diagnostic target data. While sliding the range of extracting a subsequence from the learning data little by little, the dissimilarities between all the subsequences of the learning data and a subsequence extracted from the diagnostic target data are calculated, and the lowest dissimilarity is set as the dissimilarity of the subsequence extracted from the diagnostic target data. However, with this method, it is necessary to calculate dissimilarities for all the combinations of subsequences of the diagnostic target data and all the subsequences of the learning data, and thus it takes a long time to calculate dissimilarities due to the large calculation amount.
In contrast to the above method, the method described in Patent Literature 1 includes clustering the subsequences of learning data to generate a plurality of clusters in which the dissimilarities between subsequences are within a predetermined sampling error upper limit, and integrating the subsequences in each cluster to generate sample subsequences. By comparing the sample subsequences with the subsequences of the diagnostic target data, it is possible to reduce the calculation amount and shorten the time for dissimilarity calculation.